Neural Radiance Fields (NeRF): 
Given posed RGB images, NeRF and its variants [1]}, [2]} learn a neural network to synthesize novel views of complex scenes.
At inference, for any 3D location and viewing direction, it estimates a direction-independent density value \(\sigma \) , and a direction-dependent color \(c\) .
Although designed for novel view synthesis, NeRF implicitly learns the 3D geometry and view-dependent appearance of scenes making it a useful representation of shape and appearance.
While NeRFs excel at fitting a single object instance or scene, it struggles to generalize to object categories partly because objects can be in arbitrary 3D poses – thus, existing methods use pre-canonicalized datasets [3]}.
In this paper, we provide a way to canonicalze 3D pose without any supervision for objects represented as neural radiance fields.
While our method is aimed at NeRFs, it can be extended to 3D pose canonicalization of any neural field.
